
For the sake of fair comparison of SkeGAN with another GAN architecture, we propose a VAE-GAN [1]} based architecture. Since VAEs are good at representing the data in the latent space and GANs at generating data, the VAE in VAE-GAN produces meaningful representation of the data, which helps the generator in generating data close to its actual distribution. [1]} shows the success of VAE-GAN architecture for datasets such as CelebA [3]} and Labeled Faces in the Wild (LFW) [4]}. It alleviates the blurring in generated images. We hence propose VASkeGAN for sketch generation.
